Everingham, Y.L., Sexton, J., and White, Jimmy (2011) An introduction to multivariate adaptive regression splines for the cane industry. In: Proceedings of the 2011 Conference of the Australian Society of Sugar Cane Technologists. pp. 1-22. From: 2011 Conference of the Australian Society of Sugar Cane Technologists, 4-6 May 2011, Mackay, QLD, Australia.

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Abstract

Industries strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties and growing conditions throughout each region. A challenge that the cane industry faces is how to accurately extract useful information from this vast array of data to better understand and improve the production system. Data mining methods have been developed to search large data sets for hidden patterns. This paper introduces a powerful data mining method known as Multivariate Adaptive Regression Splines (MARS). By applying the MARS methodology to model CCS production data from the Herbert district, a model was produced for the 2005 harvest period. This model produced a north-south geographic separation between low and high CCS producing farms in line with recorded CCS values. The model was also able to identify farm groupings which contributed to lower, modelled CCS values, relative to other farms. A brief investigation on the isolated effects of variety was also conducted.

Item ID:	20784
Item Type:	Conference Item (Research - E1)
ISSN:	0726–0822
Keywords:	MARS, CCS, GIS, precision, agriculture, data mining
Related URLs:	Publisher
Date Deposited:	12 Mar 2012 05:49
FoR Codes:	07 AGRICULTURAL AND VETERINARY SCIENCES > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling @ 100%
SEO Codes:	82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8206 Harvesting and Packing of Plant Products > 820603 Sugar Cane (Cut for Crushing) @ 100%
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